{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-10-17T13:39:00.563105Z",
     "start_time": "2025-10-17T13:39:00.532622Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "penguins = pd.read_csv('data/penguins.csv')\n",
    "penguins.dropna(inplace=True)\n",
    "penguins.info()\n",
    "penguins.head()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 333 entries, 0 to 343\n",
      "Data columns (total 7 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   species            333 non-null    object \n",
      " 1   island             333 non-null    object \n",
      " 2   bill_length_mm     333 non-null    float64\n",
      " 3   bill_depth_mm      333 non-null    float64\n",
      " 4   flipper_length_mm  333 non-null    float64\n",
      " 5   body_mass_g        333 non-null    float64\n",
      " 6   sex                333 non-null    object \n",
      "dtypes: float64(4), object(3)\n",
      "memory usage: 20.8+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0  Adelie  Torgersen            39.1           18.7              181.0   \n",
       "1  Adelie  Torgersen            39.5           17.4              186.0   \n",
       "2  Adelie  Torgersen            40.3           18.0              195.0   \n",
       "4  Adelie  Torgersen            36.7           19.3              193.0   \n",
       "5  Adelie  Torgersen            39.3           20.6              190.0   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0       3750.0    Male  \n",
       "1       3800.0  Female  \n",
       "2       3250.0  Female  \n",
       "4       3450.0  Female  \n",
       "5       3650.0    Male  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.3</td>\n",
       "      <td>20.6</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3650.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-17T13:39:08.109484Z",
     "start_time": "2025-10-17T13:39:07.802192Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 直方图\n",
    "sns.histplot(data=penguins,x='species')"
   ],
   "id": "278f0ebb80208164",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='species', ylabel='Count'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-17T13:40:36.160034Z",
     "start_time": "2025-10-17T13:40:36.011410Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 核密度估计图\n",
    "'''\n",
    "核密度估计图（KDE,Kernel Density Estimate Plot）是一种用于显示数据分布的\n",
    "统计图表，他通过平滑直方图的方法来估计数据的概率密度函数，使得分布图看起来更\n",
    "加连续核平滑。核密度估计是一种分参数方法，用于估计随机变量的概率密度函数。其\n",
    "基本思想是，将每个数据点视为一个\"核\"（通常是高斯分布），然后将这些核的贡献\n",
    "相加以形成平滑的密度曲线。\n",
    "'''\n",
    "# 绘制喙长度的核密度估计图\n",
    "sns.kdeplot(data=penguins,x='bill_length_mm')"
   ],
   "id": "f5fd2a690597d596",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bill_length_mm', ylabel='Density'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-17T13:42:00.708174Z",
     "start_time": "2025-10-17T13:42:00.563507Z"
    }
   },
   "cell_type": "code",
   "source": "sns.histplot(data=penguins,x='bill_length_mm',kde=True)",
   "id": "d6eba2795bb2a483",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bill_length_mm', ylabel='Count'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-17T13:44:30.670444Z",
     "start_time": "2025-10-17T13:44:30.580851Z"
    }
   },
   "cell_type": "code",
   "source": [
    "'''\n",
    "计数图\n",
    "计数图用于绘制分类变量的计数分布图，显示每个类别在数据集中出现的次数，\n",
    "是分析分类数据非常直观的工具，可以快速了解类别的分布情况\n",
    "'''\n",
    "# 绘制不同岛屿企鹅数量的计数图\n",
    "sns.countplot(data=penguins,x='island')"
   ],
   "id": "ebdc612c5e0754c8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='island', ylabel='count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 6
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
